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Record W4386070789 · doi:10.11159/cist23.112

Fluid Movement in Porous Bone via Blood Pressure: A Porous Media Theory

2023· article· en· W4386070789 on OpenAlexaffvenue
K Soleimani, Ahmad Ghasemloonia, Les Jozef Sudak

Bibliographic record

VenueProceedings of the World Congress on Electrical Engineering and Computer Systems and Science · 2023
Typearticle
Languageen
FieldMedicine
TopicNeurological and metabolic disorders
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPorous mediumPorosityFluid pressureMaterials scienceComputer scienceMechanicsPhysicsComposite material

Abstract

fetched live from OpenAlex

It is widely believed that fluid flow through the network of osteocyte canaliculi is the primary factor that controls cortical bone adaptation.Due to the difficulty of considering mass exchange between different porosity sizes in cortical bone and the measurement of the influence of the blood pulsatile on interstitial fluid using poroelasticity theory, the theory of porous media is an attractive alternative for studying cortical bones.This study presents a dual porosity model with the theory of porous media including mass exchange between vascular and lacunar-canalicular porosities including blood pressure variations on the interstitial fluid.This model enables measuring and analyzing interstitial fluid's velocity in the vascular porosity (PV) and interstitial fluid's pressure in the lacunar-canalicular porosity (PLC).The results without considering blood pressure variations verify that the predicted fluid flow field follows a general pattern consistent with that obtained from earlier studies.Taking blood pressure pulses into consideration changes the velocity and pressure fields of the interstitial fluid within the cortical bone.In addition, this method has the potential to consider mass exchange between the solid and fluid phases in relation to chemical reactions within the cortical bone.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.806
Threshold uncertainty score0.340

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.006
GPT teacher head0.199
Teacher spread0.193 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueProceedings of the World Congress on Electrical Engineering and Computer Systems and ScienceSame topicNeurological and metabolic disordersFrench-language works237,207